Essentia Institute of Rural Health, Duluth, Minnesota.
North American Medical Affairs.
Pain Med. 2017 Oct 1;18(10):1952-1960. doi: 10.1093/pm/pnw283.
Clinical guidelines for the use of opioids in chronic noncancer pain recommend assessing risk for aberrant drug-related behaviors prior to initiating opioid therapy. Despite recent dramatic increases in prescription opioid misuse and abuse, use of screening tools by clinicians continues to be underutilized. This research evaluated natural language processing (NLP) together with other data extraction techniques for risk assessment of patients considered for opioid therapy as a means of predicting opioid abuse.
Using a retrospective cohort of 3,668 chronic noncancer pain patients with at least one opioid agreement between January 1, 2007, and December 31, 2012, we examined the availability of electronic health record structured and unstructured data to populate the Opioid Risk Tool (ORT) and other selected outcomes. Clinician-documented opioid agreement violations in the clinical notes were determined using NLP techniques followed by manual review of the notes.
Confirmed through manual review, the NLP algorithm had 96.1% sensitivity, 92.8% specificity, and 92.6% positive predictive value in identifying opioid agreement violation. At the time of most recent opioid agreement, automated ORT identified 42.8% of patients as at low risk, 28.2% as at moderate risk, and 29.0% as at high risk for opioid abuse. During a year following the agreement, 22.5% of patients had opioid agreement violations. Patients classified as high risk were three times more likely to violate opioid agreements compared with those with low/moderate risk.
Our findings suggest that NLP techniques have potential utility to support clinicians in screening chronic noncancer pain patients considered for long-term opioid therapy.
慢性非癌痛患者使用阿片类药物的临床指南建议在开始阿片类药物治疗前评估异常药物相关行为的风险。尽管最近处方类阿片药物滥用急剧增加，但临床医生对筛查工具的使用仍然不足。本研究评估了自然语言处理（NLP）与其他数据提取技术相结合，用于评估考虑接受阿片类药物治疗的患者的风险，作为预测阿片类药物滥用的一种手段。
使用 2007 年 1 月 1 日至 2012 年 12 月 31 日期间至少有一份阿片类药物协议的 3668 例慢性非癌痛患者的回顾性队列，我们检查了电子健康记录的结构化和非结构化数据是否可用于填充阿片类药物风险工具（ORT）和其他选定的结果。使用 NLP 技术并结合对记录的手动审查，确定了临床记录中记录的医师阿片类药物协议违规情况。
通过手动审查证实，NLP 算法在识别阿片类药物协议违规方面具有 96.1%的灵敏度、92.8%的特异性和 92.6%的阳性预测值。在最近一次阿片类药物协议时，自动 ORT 确定 42.8%的患者为低风险、28.2%为中风险、29.0%为高风险。在协议后的一年中，22.5%的患者有阿片类药物协议违规。与低/中风险患者相比，高风险患者违反阿片类药物协议的可能性高三倍。
我们的研究结果表明，NLP 技术具有支持临床医生对考虑长期接受阿片类药物治疗的慢性非癌痛患者进行筛查的潜在应用价值。
